{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [CIFAR-100数据集上基于Vision Transformer 实现图片分类](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/cv/image_classification_ViT.html)\n",
    "\n",
    "![](https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/practices/cv/sample_img/image_classification_ViT_1.jpg)\n",
    "\n",
    "ViT就是Vision Transformer，出自论文《[An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929)》。在NLP中，输入Transformer中的是一个序列，而在视觉领域中，需要考虑如何将2D的图片转化成1D的序列。ViT尽量保持NLP中Transformer结构，做尽量小的结构或代码调整，主要目的是证明NLP中的Transformer能够很好的迁移到视觉任务上。下述步骤简要阐述了ViT的实现流程：\n",
    "\n",
    "* 使用图像切片的方式将图片转变成一组Patch得到一个序列；\n",
    "\n",
    "* 将这一序列Patch通过线性投影层(Patch Embedding)将每一个Patch转变成一个向量(token)；\n",
    "\n",
    "* 由于每个Patch在图像中具有位置信息，因此为每个token增加位置编码(Position Embedding)，这样每个token既包含了图像块原本有的图像信息，又包含了图像块所在的位置信息。此外，额外添加一个分类字符(Extra learnable class embedding, 借鉴NLP领域)，将分类字符的输出作为最终的分类判断【作者发现采用class embedding和采用global average pooling得到的结果区别不大】；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/miniconda3/envs/paddle/lib/python3.8/site-packages/paddle/utils/cpp_extension/extension_utils.py:711: UserWarning: No ccache found. Please be aware that recompiling all source files may be required. You can download and install ccache from: https://github.com/ccache/ccache/blob/master/doc/INSTALL.md\n",
      "  warnings.warn(warning_message)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0.0-rc0\n"
     ]
    }
   ],
   "source": [
    "import paddle\n",
    "import time\n",
    "import paddle.nn as nn\n",
    "import paddle.nn.functional as F\n",
    "import paddle.vision.transforms as transforms\n",
    "from paddle.io import DataLoader\n",
    "import numpy as np\n",
    "import paddle.optimizer.lr as lrScheduler\n",
    "from paddle.vision.transforms import BaseTransform\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from tqdm import tqdm\n",
    "import os\n",
    "\n",
    "paddle.seed(1024)\n",
    "np.random.seed(1234)\n",
    "\n",
    "print(paddle.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集数量: 50000 训练集图像尺寸 (3, 32, 32)\n",
      "测试集数量: 10000 测试集图像尺寸 (3, 32, 32)\n"
     ]
    }
   ],
   "source": [
    "class AutoTransforms(BaseTransform):\n",
    "    def __init__(self, transforms=None, keys=None):\n",
    "        super().__init__(keys)\n",
    "        self.transforms = transforms\n",
    "    \n",
    "    def _apply_image(self, image):\n",
    "        if self.transforms is None:\n",
    "            return image\n",
    "        choose = np.random.randint(0, len(self.transforms))\n",
    "        return self.transforms[choose](image)\n",
    "\n",
    "\n",
    "# 训练集数据增强\n",
    "mean = [0.5071, 0.4867, 0.4408]\n",
    "std = [0.2675, 0.2565, 0.2761]\n",
    "\n",
    "transforms_list = [\n",
    "    transforms.BrightnessTransform(0.5),\n",
    "    transforms.SaturationTransform(0.5),\n",
    "    transforms.ContrastTransform(0.5),\n",
    "    transforms.HueTransform(0.5),\n",
    "    transforms.RandomRotation(15, expand=True, fill=128),\n",
    "    transforms.ColorJitter(0.5, 0.5, 0.5, 0.5),\n",
    "    transforms.Grayscale(3),\n",
    "]\n",
    "\n",
    "train_tx = transforms.Compose(\n",
    "    [\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        AutoTransforms(transforms_list),\n",
    "        transforms.RandomCrop(32),\n",
    "        transforms.RandomVerticalFlip(),\n",
    "        transforms.Transpose(),\n",
    "        transforms.Normalize(0.0, 255.0),\n",
    "        transforms.Normalize(mean, std),\n",
    "    ]\n",
    ")\n",
    "\n",
    "val_tx = transforms.Compose(\n",
    "    [\n",
    "        transforms.Transpose(),\n",
    "        transforms.Normalize(0.0, 255.0),\n",
    "        transforms.Normalize(mean, std),\n",
    "    ]\n",
    ")\n",
    "\n",
    "cifar100_train = paddle.vision.datasets.Cifar100(\n",
    "    mode=\"train\", transform=train_tx, download=True\n",
    ")\n",
    "cifar100_test = paddle.vision.datasets.Cifar100(\n",
    "    mode=\"test\", transform=val_tx, download=True\n",
    ")\n",
    "\n",
    "print(\n",
    "    \"训练集数量:\",\n",
    "    len(cifar100_train),\n",
    "    \"训练集图像尺寸\",\n",
    "    cifar100_train[0][0].shape,\n",
    ")\n",
    "print(\n",
    "    \"测试集数量:\",\n",
    "    len(cifar100_test),\n",
    "    \"测试集图像尺寸\",\n",
    "    cifar100_test[0][0].shape,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def anti_normalize(image):\n",
    "    image = paddle.to_tensor(image)\n",
    "    t_mean = paddle.to_tensor(mean).reshape([3, 1, 1]).expand([3, 32, 32])\n",
    "    t_std = paddle.to_tensor(std).reshape([3, 1, 1]).expand([3, 32, 32])\n",
    "    return (image * t_std + t_mean).transpose([1, 2, 0])\n",
    "\n",
    "\n",
    "def plot_num_images(num, data):\n",
    "    if num < 1:\n",
    "        print(\"INFO:The number of input pictures must be greater than zero!\")\n",
    "    else:\n",
    "        choose_list = []\n",
    "        for i in range(num):\n",
    "            choose_n = np.random.randint(len(data))\n",
    "            choose_list.append(choose_n)\n",
    "        fig = plt.gcf()\n",
    "        fig.set_size_inches(10, 10)\n",
    "        for i in range(num):\n",
    "            ax_img = plt.subplot(\n",
    "                math.ceil(num / int(math.sqrt(num))), int(math.sqrt(num)), i + 1\n",
    "            )\n",
    "            single_data = data[choose_list[i]]\n",
    "            plt_img = anti_normalize(single_data[0])\n",
    "            ax_img.imshow(plt_img, cmap=\"binary\")\n",
    "            ax_img.set_title(\"label:\" + str(single_data[1]), fontsize=10)\n",
    "            ax_img.axis(\"off\")\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0120 18:08:15.149243 13804 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 8.6, Driver API Version: 12.2, Runtime API Version: 11.8\n",
      "W0120 18:08:15.150599 13804 gpu_resources.cc:164] device: 0, cuDNN Version: 8.9.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_num_images(25, cifar100_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_num_images(25, cifar100_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四、模型组网\n",
    "ViT模型组网部分包含图像切片(Patches)，多层感知机(MLP)，多头自注意力机制(MultiHeadSelfAttention)以及Transformer编码器(Transformer Encoder)。\n",
    "### 4.1 Patches\n",
    "Patches的目的是实现图像切块，将整张图像分割成一个个小块（patch），以方便后续将图像编码成一个个tokens。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Patches(paddle.nn.Layer):\n",
    "    def __init__(self, patch_size):\n",
    "        super().__init__()\n",
    "        self.patch_size = patch_size\n",
    "    \n",
    "    def forward(self, images):\n",
    "        patches = F.unfold(images, self.patch_size, self.patch_size)\n",
    "        return patches.transpose([0, 2, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/miniconda3/envs/paddle/lib/python3.8/site-packages/paddle/tensor/creation.py:943: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach(), rather than paddle.to_tensor(sourceTensor).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image size: 32 X 32\n",
      "Patch size: 8 X 8\n",
      "Patches per image: 16\n",
      "Elements per patch: 192\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 17 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_size = 32\n",
    "patch_size = 8\n",
    "\n",
    "image = anti_normalize(\n",
    "    paddle.to_tensor(cifar100_test[np.random.choice(len(cifar100_test))][0])\n",
    ")\n",
    "fig = plt.figure(figsize=(8, 4))\n",
    "grid = plt.GridSpec(4, 8, wspace=0.5, figure=fig)\n",
    "plt.subplot(grid[:4, :4])\n",
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "\n",
    "patches = Patches(patch_size)(image.transpose([2, 0, 1]).unsqueeze(0))\n",
    "\n",
    "print(f\"Image size: {image_size} X {image_size}\")\n",
    "print(f\"Patch size: {patch_size} X {patch_size}\")\n",
    "print(f\"Patches per image: {patches.shape[1]}\")\n",
    "print(f\"Elements per patch: {patches.shape[-1]}\")\n",
    "\n",
    "for i, patch in enumerate(patches[0]):\n",
    "    plt.subplot(grid[i // 4, i % 4 + 4])\n",
    "    patch_img = patch.reshape([3, patch_size, patch_size]).transpose([1, 2, 0])\n",
    "    plt.imshow(patch_img)\n",
    "    plt.axis(\"off\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Mlp(nn.Layer):\n",
    "    def __init__(self, feats, mlp_hidden, dropout=0.1):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(feats, mlp_hidden)\n",
    "        self.fc2 = nn.Linear(mlp_hidden, feats)\n",
    "        self.act = nn.GELU()\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.fc1(x)\n",
    "        x = self.act(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.dropout(x)\n",
    "\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Multi-Head Self Attention(MSA)\n",
    "多头自注意力机制。每个token的维度是768，假如设置12个头，每个头的维度为768/12=64\n",
    "，得到Q,K,V=197×64\n",
    "维度。最后将12个头的输出拼接起来最后得到197×768\n",
    "维度。论文发现MSA在网络刚开始时就能注意到全局上的信息，而不像CNN一样在浅层部分的感受野非常小。此外，在网络的深层部分，MSA的距离可以非常远，能够学到带有语义性的概念，而不像CNN一样靠临近的像素点去获取语义信息。\n",
    "![](https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/practices/cv/sample_img/image_classification_ViT_3.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiHeadSelfAttention(nn.Layer):\n",
    "    def __init__(self, feats, head=8, dropout=0.0, attn_dropout=0.0):\n",
    "        super(MultiHeadSelfAttention, self).__init__()\n",
    "        self.head = head\n",
    "        self.feats = feats\n",
    "        self.sqrt_d = self.feats**0.5\n",
    "        self.qkv = nn.Linear(feats, feats * 3)\n",
    "        self.out = nn.Linear(feats, feats)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.attn_dropout = nn.Dropout(attn_dropout)\n",
    "\n",
    "    def transpose_multi_head(self, x):\n",
    "        new_shape = x.shape[:-1] + [self.head, self.feats // self.head]\n",
    "        x = x.reshape(new_shape)\n",
    "        x = x.transpose([0, 2, 1, 3])\n",
    "        return x\n",
    "    \n",
    "    def forward(self, x):\n",
    "        b, n, f = x.shape\n",
    "        qkv = self.qkv(x).chunk(3, -1)\n",
    "        q, k, v = map(self.transpose_multi_head, qkv)\n",
    "        attn = F.softmax(\n",
    "            paddle.einsum(\"bhif, bhjf->bhij\", q, k) / self.sqrt_d, axis=-1\n",
    "        )\n",
    "        attn = self.attn_dropout(attn)\n",
    "        attn = paddle.einsum(\"bhij, bhjf->bihf\", attn, v)\n",
    "        out = self.dropout(self.out(attn.flatten(2)))\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TransformerEncoder(nn.Layer):\n",
    "    def __init__(\n",
    "        self, feats, mlp_hidden, head=8, dropout=0.0, attn_dropout=0.0\n",
    "    ):\n",
    "        super(TransformerEncoder, self).__init__()\n",
    "        self.layer1 = nn.LayerNorm(feats)\n",
    "        self.msa = MultiHeadSelfAttention(\n",
    "            feats, head=head, dropout=dropout, attn_dropout=attn_dropout\n",
    "        )\n",
    "        self.layer2 = nn.LayerNorm(feats)\n",
    "        self.mlp = Mlp(feats, mlp_hidden)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = self.msa(self.layer1(x)) + x\n",
    "        out = self.mlp(self.layer2(out)) + out\n",
    "        return out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ViT(nn.Layer):\n",
    "    def __init__(\n",
    "        self,\n",
    "        in_c=3,\n",
    "        num_classes=10,\n",
    "        img_size=32,\n",
    "        patch=8,\n",
    "        dropout=0.0,\n",
    "        attn_dropout=0.0,\n",
    "        num_layers=7,\n",
    "        hidden=384,\n",
    "        mlp_hidden=384 * 4,\n",
    "        head=8,\n",
    "        is_cls_token=True,\n",
    "    ):\n",
    "        super(ViT, self).__init__()\n",
    "        self.patch = patch\n",
    "        self.is_cls_token = is_cls_token\n",
    "        self.patch_size = img_size // self.patch\n",
    "        self.patches = Patches(self.patch_size)\n",
    "        f = (img_size // self.patch) ** 2 * 3\n",
    "        num_tokens = (\n",
    "            (self.patch**2) + 1 if self.is_cls_token else (self.patch**2)\n",
    "        )\n",
    "\n",
    "        self.emb = nn.Linear(f, hidden)\n",
    "        self.cls_token = (\n",
    "            paddle.create_parameter(\n",
    "                shape=[1, 1, hidden],\n",
    "                dtype=\"float32\",\n",
    "                default_initializer=nn.initializer.Assign(\n",
    "                    paddle.randn([1, 1, hidden])\n",
    "                ),\n",
    "            )\n",
    "            if is_cls_token\n",
    "            else None\n",
    "        )\n",
    "\n",
    "        self.pos_embedding = paddle.create_parameter(\n",
    "            shape=[1, num_tokens, hidden],\n",
    "            dtype=\"float32\",\n",
    "            default_initializer=nn.initializer.Assign(\n",
    "                paddle.randn([1, num_tokens, hidden])\n",
    "            ),\n",
    "        )\n",
    "\n",
    "        encoder_list = [\n",
    "            TransformerEncoder(\n",
    "                hidden,\n",
    "                mlp_hidden=mlp_hidden,\n",
    "                dropout=dropout,\n",
    "                attn_dropout=attn_dropout,\n",
    "                head=head,\n",
    "            )\n",
    "            for _ in range(num_layers)\n",
    "        ]\n",
    "        self.encoder = nn.Sequential(*encoder_list)\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.LayerNorm(hidden),\n",
    "            nn.Linear(hidden, num_classes),  # for cls_token\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = self.patches(x)\n",
    "        out = self.emb(out)\n",
    "        if self.is_cls_token:\n",
    "            out = paddle.concat(\n",
    "                [self.cls_token.tile([out.shape[0], 1, 1]), out], axis=1\n",
    "            )\n",
    "        out = out + self.pos_embedding\n",
    "        out = self.encoder(out)\n",
    "        if self.is_cls_token:\n",
    "            out = out[:, 0]\n",
    "        else:\n",
    "            out = out.mean(1)\n",
    "        out = self.fc(out)\n",
    "        return out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LabelSmoothingCrossEntropyLoss(nn.Layer):\n",
    "    def __init__(self, classes, smoothing=0.0, dim=-1):\n",
    "        super(LabelSmoothingCrossEntropyLoss, self).__init__()\n",
    "        self.confidence = 1.0 - smoothing\n",
    "        self.smoothing = smoothing\n",
    "        self.cls = classes\n",
    "        self.dim = dim\n",
    "\n",
    "    def forward(self, pred, target):\n",
    "        pred = F.log_softmax(pred, axis=self.dim)\n",
    "        with paddle.no_grad():\n",
    "            true_dist = paddle.ones_like(pred)\n",
    "            true_dist.fill_(self.smoothing / (self.cls - 1))\n",
    "            true_dist.put_along_axis_(target.unsqueeze(1), self.confidence, 1)\n",
    "        return paddle.mean(paddle.sum(-true_dist * pred, axis=self.dim))\n",
    "\n",
    "\n",
    "def get_scheduler(epochs, warmup_epochs, learning_rate):\n",
    "    base_scheduler = lrScheduler.CosineAnnealingDecay(\n",
    "        learning_rate=learning_rate, T_max=epochs, eta_min=1e-5, verbose=False\n",
    "    )\n",
    "    scheduler = lrScheduler.LinearWarmup(\n",
    "        base_scheduler,\n",
    "        warmup_epochs,\n",
    "        1e-5,\n",
    "        learning_rate,\n",
    "        last_epoch=-1,\n",
    "        verbose=False,\n",
    "    )\n",
    "    return scheduler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "schedulerTest = get_scheduler(epochs=100, warmup_epochs=5, learning_rate=1e-3)\n",
    "lr = []\n",
    "for epoch in range(100):\n",
    "    lr.append(schedulerTest.get_lr())\n",
    "    schedulerTest.step()\n",
    "plt.plot(lr)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------------------------------\n",
      "      Layer (type)           Input Shape          Output Shape         Param #    \n",
      "====================================================================================\n",
      "       Patches-2           [[1, 3, 32, 32]]       [1, 64, 48]             0       \n",
      "        Linear-1            [[1, 64, 48]]         [1, 64, 384]         18,816     \n",
      "      LayerNorm-1           [[1, 65, 384]]        [1, 65, 384]           768      \n",
      "        Linear-2            [[1, 65, 384]]       [1, 65, 1152]         443,520    \n",
      "       Dropout-2          [[1, 12, 65, 65]]     [1, 12, 65, 65]           0       \n",
      "        Linear-3            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "       Dropout-1            [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "MultiHeadSelfAttention-1    [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "      LayerNorm-2           [[1, 65, 384]]        [1, 65, 384]           768      \n",
      "        Linear-4            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "         GELU-1             [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "       Dropout-3            [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "        Linear-5            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "         Mlp-1              [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "  TransformerEncoder-1      [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "      LayerNorm-3           [[1, 65, 384]]        [1, 65, 384]           768      \n",
      "        Linear-6            [[1, 65, 384]]       [1, 65, 1152]         443,520    \n",
      "       Dropout-5          [[1, 12, 65, 65]]     [1, 12, 65, 65]           0       \n",
      "        Linear-7            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "       Dropout-4            [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "MultiHeadSelfAttention-2    [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "      LayerNorm-4           [[1, 65, 384]]        [1, 65, 384]           768      \n",
      "        Linear-8            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "         GELU-2             [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "       Dropout-6            [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "        Linear-9            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "         Mlp-2              [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "  TransformerEncoder-2      [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "      LayerNorm-5           [[1, 65, 384]]        [1, 65, 384]           768      \n",
      "       Linear-10            [[1, 65, 384]]       [1, 65, 1152]         443,520    \n",
      "       Dropout-8          [[1, 12, 65, 65]]     [1, 12, 65, 65]           0       \n",
      "       Linear-11            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "       Dropout-7            [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "MultiHeadSelfAttention-3    [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "      LayerNorm-6           [[1, 65, 384]]        [1, 65, 384]           768      \n",
      "       Linear-12            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "         GELU-3             [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "       Dropout-9            [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "       Linear-13            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "         Mlp-3              [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "  TransformerEncoder-3      [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "      LayerNorm-7           [[1, 65, 384]]        [1, 65, 384]           768      \n",
      "       Linear-14            [[1, 65, 384]]       [1, 65, 1152]         443,520    \n",
      "       Dropout-11         [[1, 12, 65, 65]]     [1, 12, 65, 65]           0       \n",
      "       Linear-15            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "       Dropout-10           [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "MultiHeadSelfAttention-4    [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "      LayerNorm-8           [[1, 65, 384]]        [1, 65, 384]           768      \n",
      "       Linear-16            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "         GELU-4             [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "       Dropout-12           [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "       Linear-17            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "         Mlp-4              [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "  TransformerEncoder-4      [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "      LayerNorm-9           [[1, 65, 384]]        [1, 65, 384]           768      \n",
      "       Linear-18            [[1, 65, 384]]       [1, 65, 1152]         443,520    \n",
      "       Dropout-14         [[1, 12, 65, 65]]     [1, 12, 65, 65]           0       \n",
      "       Linear-19            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "       Dropout-13           [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "MultiHeadSelfAttention-5    [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "      LayerNorm-10          [[1, 65, 384]]        [1, 65, 384]           768      \n",
      "       Linear-20            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "         GELU-5             [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "       Dropout-15           [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "       Linear-21            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "         Mlp-5              [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "  TransformerEncoder-5      [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "      LayerNorm-11          [[1, 65, 384]]        [1, 65, 384]           768      \n",
      "       Linear-22            [[1, 65, 384]]       [1, 65, 1152]         443,520    \n",
      "       Dropout-17         [[1, 12, 65, 65]]     [1, 12, 65, 65]           0       \n",
      "       Linear-23            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "       Dropout-16           [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "MultiHeadSelfAttention-6    [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "      LayerNorm-12          [[1, 65, 384]]        [1, 65, 384]           768      \n",
      "       Linear-24            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "         GELU-6             [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "       Dropout-18           [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "       Linear-25            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "         Mlp-6              [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "  TransformerEncoder-6      [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "      LayerNorm-13          [[1, 65, 384]]        [1, 65, 384]           768      \n",
      "       Linear-26            [[1, 65, 384]]       [1, 65, 1152]         443,520    \n",
      "       Dropout-20         [[1, 12, 65, 65]]     [1, 12, 65, 65]           0       \n",
      "       Linear-27            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "       Dropout-19           [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "MultiHeadSelfAttention-7    [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "      LayerNorm-14          [[1, 65, 384]]        [1, 65, 384]           768      \n",
      "       Linear-28            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "         GELU-7             [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "       Dropout-21           [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "       Linear-29            [[1, 65, 384]]        [1, 65, 384]         147,840    \n",
      "         Mlp-7              [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "  TransformerEncoder-7      [[1, 65, 384]]        [1, 65, 384]            0       \n",
      "      LayerNorm-15            [[1, 384]]            [1, 384]             768      \n",
      "       Linear-30              [[1, 384]]            [1, 100]           38,500     \n",
      "====================================================================================\n",
      "Total params: 6,278,116\n",
      "Trainable params: 6,278,116\n",
      "Non-trainable params: 0\n",
      "------------------------------------------------------------------------------------\n",
      "Input size (MB): 0.01\n",
      "Forward/backward pass size (MB): 21.58\n",
      "Params size (MB): 23.95\n",
      "Estimated Total Size (MB): 45.55\n",
      "------------------------------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'total_params': 6278116, 'trainable_params': 6278116}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model = ViT(\n",
    "    in_c=3,\n",
    "    num_classes=100,\n",
    "    img_size=32,\n",
    "    patch=8,\n",
    "    dropout=0.5,\n",
    "    attn_dropout=0.1,\n",
    "    num_layers=7,\n",
    "    hidden=384,\n",
    "    head=12,\n",
    "    mlp_hidden=384,\n",
    "    is_cls_token=True,\n",
    ")\n",
    "paddle.summary(Model, (1, 3, 32, 32))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 200\n",
    "BATCH_SIZE = 128\n",
    "NUM_CLASSES = 100\n",
    "WARMUP_EPOCHS = 5\n",
    "LR = 1e-3\n",
    "\n",
    "scheduler = get_scheduler(\n",
    "    epochs=EPOCHS, warmup_epochs=WARMUP_EPOCHS, learning_rate=LR\n",
    ")\n",
    "optim = paddle.optimizer.Adam(\n",
    "    learning_rate=scheduler, parameters=Model.parameters(), weight_decay=5e-5\n",
    ")\n",
    "criterion = LabelSmoothingCrossEntropyLoss(NUM_CLASSES, smoothing=0.1)\n",
    "\n",
    "train_loader = DataLoader(\n",
    "    cifar100_train,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=True,\n",
    "    num_workers=0,\n",
    "    drop_last=False,\n",
    ")\n",
    "test_loader = DataLoader(\n",
    "    cifar100_test,\n",
    "    batch_size=BATCH_SIZE * 16,\n",
    "    shuffle=False,\n",
    "    num_workers=0,\n",
    "    drop_last=False,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_epoch(model, epoch, interval=20):\n",
    "    acc_num = 0\n",
    "    total_samples = 0\n",
    "    nb = len(train_loader)\n",
    "    pbar = enumerate(train_loader)\n",
    "    pbar = tqdm(\n",
    "        pbar, total=nb, colour=\"red\", disable=((epoch + 1) % interval != 0)\n",
    "    )\n",
    "    pbar.set_description(f\"EPOCH: {epoch:3d}\")\n",
    "    for _, (_, data) in enumerate(pbar):\n",
    "        x_data = data[0]\n",
    "        y_data = data[1]\n",
    "        predicts = model(x_data)\n",
    "        loss = criterion(predicts, y_data)\n",
    "        loss_item = loss.item()\n",
    "        acc_num += paddle.sum(predicts.argmax(1) == y_data).item()\n",
    "        total_samples += y_data.shape[0]\n",
    "        total_acc = acc_num / total_samples\n",
    "        current_lr = optim.get_lr()\n",
    "        loss.backward()\n",
    "        pbar.set_postfix(\n",
    "            train_loss=f\"{loss_item:5f}\",\n",
    "            train_acc=f\"{total_acc:5f}\",\n",
    "            train_lr=f\"{current_lr:5f}\",\n",
    "        )\n",
    "        optim.step()\n",
    "        optim.clear_grad()\n",
    "    scheduler.step()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "@paddle.no_grad()\n",
    "def validation(model, epoch, interval=20):\n",
    "    model.eval()\n",
    "    acc_num = 0\n",
    "    total_samples = 0\n",
    "    nb = len(test_loader)\n",
    "    pbar = enumerate(test_loader)\n",
    "    pbar = tqdm(\n",
    "        pbar, total=nb, colour=\"green\", disable=((epoch + 1) % interval != 0)\n",
    "    )\n",
    "    pbar.set_description(f\"EVAL\")\n",
    "    for _, (_, data) in enumerate(pbar):\n",
    "        x_data = data[0]\n",
    "        y_data = data[1]\n",
    "        predicts = model(x_data)\n",
    "        acc_num += paddle.sum(predicts.argmax(1) == y_data).item()\n",
    "        total_samples += y_data.shape[0]\n",
    "        batch_acc = paddle.metric.accuracy(predicts, y_data.unsqueeze(1)).item()\n",
    "        total_acc = acc_num / total_samples\n",
    "        pbar.set_postfix(\n",
    "            eval_batch_acc=f\"{batch_acc:4f}\", total_acc=f\"{total_acc:4f}\"\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_13804/2798264585.py\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEPOCHS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mtrain_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mModel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mvalidation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mModel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m50\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_13804/3706526725.py\u001b[0m in \u001b[0;36mtrain_epoch\u001b[0;34m(model, epoch, interval)\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0mtotal_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0macc_num\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mtotal_samples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0mcurrent_lr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_lr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m         pbar.set_postfix(\n\u001b[1;32m     22\u001b[0m             \u001b[0mtrain_loss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"{loss_item:5f}\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/miniconda3/envs/paddle/lib/python3.8/site-packages/decorator.py\u001b[0m in \u001b[0;36mfun\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    230\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkwsyntax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mcaller\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextras\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m     \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m     \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/miniconda3/envs/paddle/lib/python3.8/site-packages/paddle/base/wrapped_decorator.py\u001b[0m in \u001b[0;36m__impl__\u001b[0;34m(func, *args, **kwargs)\u001b[0m\n\u001b[1;32m     38\u001b[0m     ) -> _RetT2:\n\u001b[1;32m     39\u001b[0m         \u001b[0mwrapped_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdecorator_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m__impl__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/miniconda3/envs/paddle/lib/python3.8/site-packages/paddle/base/framework.py\u001b[0m in \u001b[0;36m__impl__\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    702\u001b[0m             \u001b[0min_dygraph_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m         ), f\"We only support '{func.__name__}()' in dynamic graph mode, please call 'paddle.disable_static()' to enter dynamic graph mode.\"\n\u001b[0;32m--> 704\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    705\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    706\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m__impl__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/miniconda3/envs/paddle/lib/python3.8/site-packages/paddle/base/dygraph/tensor_patch_methods.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, grad_tensor, retain_graph)\u001b[0m\n\u001b[1;32m    355\u001b[0m                 \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_grad_scalar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m             \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_backward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0min_profiler_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "for epoch in range(EPOCHS):\n",
    "    train_epoch(Model, epoch)\n",
    "    validation(Model, epoch)\n",
    "    if (epoch + 1) % 50 == 0:\n",
    "        paddle.save(Model.state_dict(), \"../output/\" + str(epoch + 1) + \".pdparams\")\n",
    "paddle.save(Model.state_dict(), \"../output/\" + \"finished.pdparams\")\n",
    "end = time.time()\n",
    "print(\"Training Cost \", (end - start) / 60, \"minutes\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_dict = paddle.load(\"../output/\" + \"finished.pdparams\")\n",
    "Model.set_state_dict(state_dict)\n",
    "Model.eval()\n",
    "top1_num = 0\n",
    "top5_num = 0\n",
    "total_samples = 0\n",
    "nb = len(test_loader)\n",
    "pbar = enumerate(test_loader)\n",
    "pbar = tqdm(pbar, total=nb, colour=\"green\")\n",
    "pbar.set_description(f\"EVAL\")\n",
    "with paddle.no_grad():\n",
    "    for _, (_, data) in enumerate(pbar):\n",
    "        x_data = data[0]\n",
    "        y_data = data[1]\n",
    "        predicts = Model(x_data)\n",
    "        total_samples += y_data.shape[0]\n",
    "        top1_num += (\n",
    "            paddle.metric.accuracy(predicts, y_data.unsqueeze(1), k=1).item()\n",
    "            * y_data.shape[0]\n",
    "        )\n",
    "        top5_num += (\n",
    "            paddle.metric.accuracy(predicts, y_data.unsqueeze(1), k=5).item()\n",
    "            * y_data.shape[0]\n",
    "        )\n",
    "        TOP1 = top1_num / total_samples\n",
    "        TOP5 = top5_num / total_samples\n",
    "        pbar.set_postfix(TOP1=f\"{TOP1:4f}\", TOP5=f\"{TOP5:4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Model.eval()\n",
    "data = cifar100_test[np.random.randint(0, len(cifar100_test))]\n",
    "pred = Model(paddle.to_tensor(data[0]).unsqueeze(0)).argmax(-1).item()\n",
    "print(\"预测结果为:\", pred, \"标签为:\", data[1].item())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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